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突发台风事件网络舆情预警机制研究

Research on Network Public Opinion Warning Mechanism for Sudden Typhoon Events
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摘要 [研究目的]随着网络社会的不断发展,网络舆情危机不断显现,舆情预警机制在应对突发事件中发挥的作用日益显著。对突发台风事件网络舆情预警机制进行研究,可为政府争取防范时间,降低社会舆论危机。[研究方法]该文在理论分析、文献调查的基础上,建立了舆情预警指标体系,并运用组合赋权法对指标进行定权后采用白化权函数和SAGAFCM算法来分级分类。[研究结论]Ⅰ~Ⅳ级风险指标实时值和Ⅰ’~Ⅳ’级风险指标实时值与舆情危机预警启动概率存在非线性关系,并且后者对其概率影响更大;同时Ⅰ’级、Ⅱ’级、Ⅲ’级风险指标实时值的交互关系产生了预警临界曲面,为政府部门采取相应措施提供了参考。 [Research purpose]With the continuous development of the network society,the network public opinion crisis is emerging,and the public opinion early warning mechanism plays an increasingly significant role in responding to emergencies.Research on the network public opinion early warning mechanism of sudden typhoon events can win the time for the government to prevent and reduce the public opinion crisis.[Research method]On the basis of theoretical analysis and literature survey,this study establishes a public opinion early warning indicator system,uses the combination weighting method to compute the weight of the indicators,and then uses the whitening weight function and SAGAFCM algorithm to classify them.[Research conclusion]There is a nonlinear relationships between the real-time value of the I-IV level risk indicators,the real-time value of the I'-IV'level risk indicators,and the probability of public opinion crisis early warning,and the latter has a greater impact on its probability;At the same time,the interaction between the real-time values of the risk indicators of Level I',Level II'and Level III'produces the warning critical surface,which provides a reference for the government departments to take corresponding measures.
作者 王建华 朱敏 郝婷婷 林超英 Wang Jianhua;Zhu Min;Hao Tingting;Lin Chaoying(School of Bussiness,Jiangnan University,Wuxi 214122)
机构地区 江南大学商学院
出处 《情报杂志》 北大核心 2024年第1期152-159,F0003,共9页 Journal of Intelligence
基金 国家社会科学基金项目“政府开放数据的应用生态及服务体系研究”(编号:19BTQ078) 广州市哲学社会科学发展“十四五”规划课题项目“广州建设数字社会和数字政府研究”(编号:2022GZYB31)研究成果。
关键词 网络舆情 舆情预警 突发事件 舆情预警指标 PETRI网 聚类分析 马尔可夫链 network public opinion public opinion warning emergencies public opinion early warning indicators Petri nets cluster analysis Markov chain
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